Update README.md

README.md

@@ -104,7 +104,7 @@ The general characteristics of this specific model **FLAIR-INC_RVBIE_resnet34_un
 - **Developed by:** IGN
 - **Compute infrastructure:** 
     - software: python, pytorch-lightning
-    - hardware: GENCI, XXX
+    - hardware: HPC/AI resources provided by GENCI-IDRIS
 - **License:** : Apache 2.0
 
   
@@ -125,13 +125,13 @@ By construction (sampling 75 domains) the model is robust to these shifts, and c
 
 _**Specification for the Elevation channel**_ :
 The fifth dimension of the RGBIE images is the Elevation (height of building and vegetation). This information is encoded in a 8-bit encoding format. 
-When decoded to [0,255] ints, a difference of 1 coresponds to 20 cm step of elevation difference.   
+When decoded to [0,255] ints, a difference of 1 should coresponds to 0.2 meters step of elevation difference.   
 
 
 _**Land Cover classes of prediction**_ :
-The orginial class nomenclature of the FLAIR Dataset is made up of 19 classes(See the [FLAIR dataset](https://huggingface.co/datasets/IGNF/FLAIR) page for details).
+The orginial class nomenclature of the FLAIR Dataset is made up of 19 classes (See the [FLAIR dataset](https://huggingface.co/datasets/IGNF/FLAIR) page for details).
 However 3 classes corresponding to uncertain labelisation (Mixed (16), Ligneous (17) and Other (19)) and 1 class with very poor labelling (Clear cut (15)) were deasctivated during training.
-As a result, the logits produced by the model are of size 19x1, but class 15,16,17 and 19 : (1) should appear at 0 in the logits (2) should never predicted in the Argmax.
+As a result, the logits produced by the model are of size 19x1, but classes n° 15, 16, 17 and 19 should appear at 0 in the logits and should never predicted in the Argmax.
 
 
 <!-- ## Bias, Risks, and Limitations -->
@@ -140,7 +140,7 @@ As a result, the logits produced by the model are of size 19x1, but class 15,16,
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 _**Using the model on input images with other spatial resolution**_ :
-The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained with fixed scale conditions.All patches used for training are derived from aerial images of 0.2 meters spatial resolution. Only flip and rotate augmentation were performed during the training process.  
+The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained with fixed scale conditions. All patches used for training are derived from aerial images of 0.2 meters spatial resolution. Only flip and rotate augmentation were performed during the training process.  
 No data augmentation method concerning scale change was used during training. The user should pay attention that generalization issues can occur while applying this model to images that have different spatial resolutions.
 
 _**Using the model for other remote sensing sensors**_ :
@@ -160,11 +160,12 @@ The user should be aware that applying the model to other type of landscapes may
 
 ## How to Get Started with the Model
 
+<!-- ANATOL }}-->
+
 Use the code below to get started with the model.
 {{ get_started_code | default("[More Information Needed]", true)}}
 
 
-
 ## Training Details
 
 ### Training Data
@@ -186,7 +187,9 @@ Here are the number of patches used for train and validation :
 #### Preprocessing [optional]
 
 For traning the model, input normalization was performed so as the input dataset has **a mean=0** and a **standard deviation = 1** channel wise. 
-We used the statistics of TRAIN+VALIDATION for input normalization. It is recommended that the user apply the same type of input normalization. Here are the statistics of the TRAIN+VALIDATIOn set :
+We used the statistics of TRAIN+VALIDATION for input normalization. It is recommended that the user apply the same type of input normalization. 
+
+Here are the statistics of the TRAIN+VALIDATIOn set :
 
 | Modalities              | Mean (Train + Validation)       |Std    (Train + Validation)     |
 | ----------------------- | ----------- |----------- |
@@ -227,7 +230,8 @@ We used the statistics of TRAIN+VALIDATION for input normalization. It is recomm
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained on a HPC/AI resources provided by GENCI-IDRIS (Grant 2022-A0131013803). 16 V100 GPUs were requested ( 4 nodes, 4 GPUS per node).
+The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained on a HPC/AI resources provided by GENCI-IDRIS (Grant 2022-A0131013803). 
+16 V100 GPUs were requested ( 4 nodes, 4 GPUS per node). With this configuration the approximate learning time is 6 minutes per epoch.
 
 FLAIR-INC_RVBIE_resnet34_unet_15cl_norm was obtained for num_epoch=76 with corresponding val_loss=0.56. 
 
@@ -250,16 +254,17 @@ FLAIR-INC_RVBIE_resnet34_unet_15cl_norm was obtained for num_epoch=76 with corre
 #### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
-The evaluation was performed on a TEST set of 31750 patches that are independant from the TRAIN and VALIDATION patches. They represent 15 spatio-temporal domains.
+The evaluation was performed on a TEST set of 31 750 patches that are independant from the TRAIN and VALIDATION patches. They represent 15 spatio-temporal domains.
 The TEST set corresponds to the reunion of the TEST set of scientific challenges FLAIR#1 and FLAIR#2. See the [FLAIR challenge page](https://ignf.github.io/FLAIR/) for more details.
 
 The choice of a separate TEST set instead of cross validation was made to be coherent with the FLAIR challenges. 
-However the metrics for the Challenge were calculated on 12 classes and the TEST set acordingly. As a result the _Snow_ class is unfortunately absent from the TEST set.
+However the metrics for the Challenge were calculated on 12 classes and the TEST set acordingly. 
+As a result the _Snow_ class is unfortunately absent from the TEST set.
 <!-- {{ testing_data | default("[More Information Needed]", true)}} -->
 
 #### Metrics
 
-With the evaluation protocol, the **FLAIR-INC_RVBIE_resnet34_unet_15cl_norm** have been evaluated to **OA= 76.37%** and **mIoU=54.71%**. 
+With the evaluation protocol, the **FLAIR-INC_RVBIE_resnet34_unet_15cl_norm** have been evaluated to **OA= 76.37%** and **mIoU=58.63%**. 
 The _snow_ class is discarded from the average metrics.
 
 The following table give the class-wise metrics :